Exploiting Purine as an Internal Standard for SERS Quantification of Purine Derivative Molecules Released by Bacteria.

Surface-enhanced Raman scattering (SERS) is a highly sensitive technique used in diverse biomedical applications including rapid antibiotic susceptibility testing (AST). However, signal fluctuation in SERS, particularly the widespread of signals measured from different batches of SERS substrates, compromises its reliability and introduces potential errors in SERS-AST. In this study, we investigate the use of purine as an internal standard (IS) to recalibrate SERS signals and quantify the concentrations of two important purine derivatives, adenine and hypoxanthine, which are the most important biomarkers used in SERS-AST. Our findings demonstrate that purine IS effectively mitigates SERS signal fluctuations and enables accurate prediction of adenine and hypoxanthine concentrations across a wide range (5 orders of magnitude). Calibrations with purine as an IS outperform those without, exhibiting a 10-fold increase in predictive accuracy. Additionally, the calibration curve obtained from the first batch of SERS substrates remains effective for 64 additional substrates fabricated over a half-year period. Measurements of adenine and hypoxanthine concentrations in bacterial supernatants using SERS with purine IS closely align with the liquid chromatography-mass spectrometry results. The use of purine as an IS offers a simple and robust platform to enhance the speed and accuracy of SERS-AST, while also paving the way for in situ SERS quantification of purine derivatives released by bacteria under various stress conditions.

Rapid detection of nicotine and benzoic acid in e-liquids with surface-enhanced Raman scattering and artificial intelligence-assisted spectrum interpretation.

Electronic cigarettes have rapidly gained acceptance recently. Nicotine-containing electronic cigarette liquids (e-liquids) are prohibited in some countries, but are permitted and simply available online in others. A rapid detection method is therefore required for on-site inspection or screening of a large amount of samples. Our previous study demonstrated a surface-enhanced Raman scattering (SERS)-based approach to identify nicotine-containing e-liquids; without any pre-treatment, e-liquid can be directly tested on our solid-phase SERS substrates, made of silver nanoparticle arrays embedded in anodic aluminium oxide nanochannels (Ag/AAO). However, this approach required manual determination of spectral signatures and negative samples should be validated in the second round detection. Here, after examining 406 commercial e-liquids, we refined this approach by developing artificial intelligence (AI)-assisted spectrum interpretations. We also found that nicotine and benzoic acid can be simultaneously detected in our platform. This increased test sensitivity because benzoic acid is usually used in nicotine salts. Around 64% of nicotine-positive samples in this study showed both signatures. Using either cutoffs of nicotine and benzoic acid peak intensities or a machine learning model based on the CatBoost algorithm, over 90% of tested samples can be correctly discriminated with only one round of SERS measurement. False negative and false positive rates were 2.5-4.4% and 4.4-8.9%, respectively, depending on the interpretation method and thresholds applied. The new approach takes only 1 microliter of sample and can be performed in 1-2 min, suitable for on-site inspection with portable Raman detectors. It could also be a complementary platform to reduce samples that need to be analyzed in the central labs and has the potential to identify other prohibited additives.

Speciation Analysis of Cr(VI) and Cr(III) in Water with Surface-Enhanced Raman Spectroscopy.

Identifying and quantifying chromium in water are important for the protection of precious water resources from chromium pollution. Standard methods however are unable to easily distinguish toxic hexavalent chromium, Cr(VI), from innocuous trivalent chromium, Cr(III), are time-consuming, or require large sample quantity. We show in this report that Cr(VI) and Cr(III) in water can be differentiated based on their distinct spectral features of surface-enhanced Raman scattering (SERS). Their SERS signals exhibit different pH dependences: the SERS features of Cr(VI) and Cr(III) are most prominent at pH values of 10 and 5.5, respectively. The obtained limit of detection of Cr(VI) in water is below 0.1 mg/L. Both concentration curves of their SERS signals show Langmuir sorption isotherm behavior. A procedure was developed to quantify Cr(VI) concentration based on the direct retrieval or addition method with an error of 10%. Finally, the SERS detection of Cr(VI) is shown to be insensitive to co-present Cr(III). The developed SERS procedure offers potential to monitor toxic chromium in fields.

Rapid identification of nicotine in electronic cigarette liquids based on surface-enhanced Raman scattering.

Nicotine-containing electronic cigarette liquid (e-liquid) is prohibited in many countries, creating requirements for rapid detection approaches for on-site inspection or screening for large amounts of samples. Here, we demonstrate a simple way to identify nicotine using surface-enhanced Raman scattering (SERS) with substrates made of silver nanoparticle arrays imbedded in anodic aluminum oxide nanochannels (Ag/AAO). Compared with the reported colloidal nanoparticle-based SERS, that required serial dilutions to enable colloid aggregation in the viscous e-liquid, a small amount of undiluted e-liquid sample can be directly added onto our solid-phase Ag/AAO substrate without any pre-treatment. The sensitivity of our SERS measurements is 2-3 orders of magnitude higher than that required for identification of nicotine in e-liquid, which is typically around 1000-18,000 ppm. Using such nanoparticle array-based SERS, we have tested 22 commercially available e-liquid products, using the corresponding gas chromatography-mass spectrometry (GC-MS) reports as the reference. The SERS measurements were done within one hour and successfully identified 20 samples. Only 2 samples showed SERS interference from ingredients that were not suitable for SERS analysis.